MUMBAI, India, May 1 -- Intellectual Property India has published a patent application (202641049561 A) filed by Malla Reddy University, Hyderabad, Telangana, on April 18, for 'federated edge intelligence framework with differential privacy for multi-modal healthcare ai.'

Inventor(s) include Shravani Amar; and Dr. Perla Anitha.

The application for the patent was published on May 1, under issue no. 18/2026.

According to the abstract released by the Intellectual Property India: "A federated edge intelligence framework integrated with differential privacy for secure and efficient multi-modal healthcare artificial intelligence. The framework enables distributed model training across multiple healthcare institutions and edge devices without sharing raw patient data, thereby preserving privacy and ensuring regulatory compliance. It integrates heterogeneous healthcare data sources, including electronic health records, medical imaging, and wearable sensor data, using advanced multi-modal data fusion techniques to improve analytical accuracy and clinical decision-making. The system employs federated learning to allow collaborative model development while maintaining data locality. Differential privacy mechanisms are applied to local model updates by introducing controlled noise, ensuring that sensitive patient information cannot be inferred during data exchange. Edge computing capabilities enable real-time data processing and low-latency inference, supporting timely healthcare interventions in both clinical and remote settings. Additionally, the framework incorporates predictive analytics models to identify disease patterns, detect anomalies, and generate proactive alerts for healthcare professionals. Secure aggregation, encryption, and authentication mechanisms ensure robust data protection throughout the system. The scalable and modular architecture supports deployment across diverse healthcare infrastructures. Overall, the invention enhances privacy, efficiency, and reliability, enabling next- generation intelligent healthcare systems with improved diagnostic accuracy and patient outcomes."

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